init vals for window size, loss window, loss cap

llmfoundry/callbacks/kill_loss_spike_callback.py

@@ -59,9 +59,9 @@ def __init__(
         self.patience = patience
         self.outlier_multiplier = outlier_multiplier
         self.outlier_counter = 0
-        self.window_size = None
-        self.loss_window = None
-        self.loss_cap = None
+        self.window_size = MIN_WINDOW_SIZE
+        self.loss_window = deque(maxlen=self.window_size)
+        self.loss_cap = float('inf')
 
     def detect_loss_spike(self, train_loss: float, running_loss_avg: float):
         # Train loss is an outlier
@@ -95,16 +95,17 @@ def detect_high_losses(self, current_step: int):
 
     def fit_start(self, state: State, logger: Logger) -> None:
         # Set the window size to a fraction of the total number of training batches for the run, minimum 100 batches.
-        if state.max_duration.unit == TimeUnit.EPOCH:
-            self.window_size = max(
-                MIN_WINDOW_SIZE,
-                round(float(state.dataloader_len * state.max_duration.value / 20)),
-            )
-        elif state.max_duration.unit == TimeUnit.BATCH or state.max_duration.unit == TimeUnit.TOKEN:
-            self.window_size = max(
-                MIN_WINDOW_SIZE,
-                round(float(state.max_duration.value / 20)),
-            )
+        if state.max_duration is not None:
+            if state.max_duration.unit == TimeUnit.EPOCH:
+                self.window_size = max(
+                    MIN_WINDOW_SIZE,
+                    round(float(state.dataloader_len * state.max_duration.value / 20)),
+                )
+            elif state.max_duration.unit == TimeUnit.BATCH or state.max_duration.unit == TimeUnit.TOKEN:
+                self.window_size = max(
+                    MIN_WINDOW_SIZE,
+                    round(float(state.max_duration.value / 20)),
+                )
         self.loss_window = deque(maxlen=self.window_size)
 
     def batch_end(self, state: State, logger: Logger) -> None: